Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.
Highlights
Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer death worldwide [1]
In CRC studies, we mainly focus on these four common types (Figure 3)
machine learning (ML) algorithms, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Neural Network (NN), and Random Forest (RF), where the wavelet transformed and Haralick coefficients were used as the feature vector for the NN classifier, resulting in the highest accuracy and kappa values of 83% and 64.7%, respectively
Summary
Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer death worldwide [1]. The(EMRs), increasing assisted endoscopy for polyp detection and characterization, and the use of high-risk prevalence of endoscopic imaging datasets and electronic medical records (EMRs), prediction usingfor clinical omics data, are expected to improve. Thanks to advanced processing technology in the eliminate differences in experience, and reduce misdiagnosis rates.image readability, field of image recognition, DL can significantly improve medical. With the help of AI, such asand neoadjuvant radiotherapy (nCRT)care andto chemotherapy, to Prognosis: Prognosis of CRC includes the predicting of recurrence and estimating improve curative effects and provide more precise medical care to patients. Cox regression model are Prognosis includesmethods the predicting recurrence and estimating of traditionally used to predict patient prognosis; data-driven ML approaches the survival period [3] Statistical methods such as the Cox regression model are allow for more effective exploitation of multidimensional data to accurately predict survival and flexibly track disease progression.
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